Search

To address the issue of low detection accuracy of steel surface defects due to complex texture background interference and complex defect morphology, this paper proposes an improved YOLOv8 model based on MobileViTv2 and Cross-Local Connection for steel surface defect detection. Firstly, the lightweight MobileViTv2 network is introduced into the backbone network, which enhances the feature extraction capability of the model in complex defect shapes by combining the advantages of CNN and Transformer. Then, the designed CLC method is introduced into the neck network, which connects deep and shallow features through additional convolutional layers, further integrating defect features in the presence of complex texture background interference. Finally, the NET-DET dataset is augmented to improve the model's robustness. Experimental results show that the mAP of the improved model is 74.1%, with a detection speed of 86.2 FPS and model memory usage of 27.5 MB. Compared to YOLOv5 and YOLOv8, the mAP of the improved model is increased by 6.5% and 4%, respectively. Compared to existing object detection models, the improved model has the characteristics of high detection accuracy and fast detection speed, meeting the requirements of industrial production for steel surface defect detection. [ABSTRACT FROM AUTHOR]

The detection of steel surface defects is of great significance to steel production. In order to better meet the requirements of accuracy, real-time, and lightweight model, this paper proposes a highly efficient and lightweight steel surface defect detection method based on YOLOv5n. Firstly, ODMobileNetV2 composed of MobileNetV2 and ODConv is used as the backbone to improve the defect feature extraction capability. Secondly, GSConv is utilized in the neck to achieve deep information fusion through channel concatenation and shuffling, enhancing the ability of feature fusion. Finally, this paper proposes a spatial-channel reconstruction block (SCRB) designed to suppress redundant features and improve the representation ability of defect features through feature separation and reconstruction. Experimental results show that this method achieves 84.1% mAP and 109 FPS on the NEU-DET dataset, and 72.9% mAP and 110.1 FPS on the GC10-DET dataset, enabling accurate and efficient detection. Furthermore, the number of parameters is only 5.04M, which has a significant lightweight advantage. [ABSTRACT FROM AUTHOR]

Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules. Firstly, because of the potential information loss that convolutional neural networks (CNN) may encounter when dealing with miniature targets, the CBiF combines CNN with Transformer to optimize local and global features. Secondly, to address the increased computational complexity caused by the extensive use of convolutional layers, the FC uses the FasterNet block to reduce redundant computations and memory access. Lastly, the EMA is incorporated into the FC to design the EFC module and enhance feature fusion capability while ensuring the light weight of the model. CFE-YOLOv8s achieves mAP@0.5 values of 77.8% and 69.5% on the NEU-DET and GC10-DET datasets, respectively, representing enhancements of 3.1% and 2.8% over YOLOv8s, with reductions of 22% and 18% in model parameters and FLOPS. The CFE-YOLOv8s demonstrates superior overall performance and balance compared to other advanced models. [ABSTRACT FROM AUTHOR]

Abstract Surface defects on steel, arising from factors like steel composition and manufacturing techniques, pose significant challenges to industrial production. Efficient and precise detection of these defects is crucial for enhancing production efficiency and product quality. In accordance with these requisites, this paper elects to undertake the detection task predicated on the you only look once (YOLO) algorithm. In this study, we propose a novel approach for surface flaw identification based on the YOLOv5 algorithm, called YOLOv5-KBS. This method integrates attention mechanism and weighted Bidirectional Feature Pyramid Network (BiFPN) into YOLOv5 architecture. Our method addresses issues of background interference and defect size variability in images. Experimental results show that the YOLOv5-KBS model achieves a notable 4.2% increase in mean Average Precision (mAP) and reaches a detection speed of 70 Frames Per Second (FPS), outperforming the baseline model. These findings underscore the effectiveness and potential applications of our proposed method in industrial settings.

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model's inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements. [ABSTRACT FROM AUTHOR]

In order to achieve rapid inference and generalization results, the majority of Convolutional Neural Network (CNN) based semantic segmentation models strive to mine high-level features that contain rich contextual semantic information. However, at steel plate defects detection scenario, some background textures' noises are similar to the foreground leading to hard distinguishment, which will significantly interfere with feature extraction. Texture features themselves often hold the most plentiful contextual information. Despite this, semantic segmentation tasks rarely take texture features into account when identifying surface defects on steel plates. In that case, the essential details, such as the edge texture and other intuitive low-level features, will generally cannot be included into the final feature map. To address the problems of inefficient accuracy and slow speed of existing detection, this study proposed a steel plate surface defect detection method using contextual information and attention mechanism, and utilizes a multi-layer feature extraction method and fusion framework based on low-level statistical textures. Through the identification of pixel-level spatial and correlation relationships, characteristics of low-level defects are extracted. Furthermore, to effectively incorporate statistical texture in CNN, a novel quantization technique has been developed. This quantization method allows for the conversion of continuous texture into various levels of intensity. The network parameters were iterated in a gradient direction, facilitating the defects division. Empirical results have demonstrated the feasibility of applying the proposed approach to practical steel plate testing. Additionally, ablation experiments have demonstrated that the method is capable of effectively enhancing surface defect detection for steel plates, resulting in industry-leading performance. [ABSTRACT FROM AUTHOR]

This paper introduces a new steel defect detection model CK-NET, which uses YOLOv9c as the baseline model and adopts YOLOv9’s model architecture for improvement. The proposed model addresses issues of shallow information loss and insufficient feature extraction and fusion caused by network deepening. A new feature extraction module is designed to control model parameters and enhance feature extraction. Minor improvements to Convolutional Block of Attention Module (CBAM) and the introduction of deformable convolutions in the backbone network further enhance feature extraction. A new feature fusion module, combined with a self-attention mechanism (SA), elegantly fuses features from different levels to assist downstream detection tasks. The Programmable Gradient Information (PGI) auxiliary branch is also improved to better fuse features and guide model learning with gradient information. All improved modules have been integrated into CK-NET. Experiments on the NEU-DET dataset demonstrate that CK-Net achieves a 13.2% higher mAP value than YOLOv9c, reaching a 92.1% mAP value while maintaining similar model parameters, validating the model’s effectiveness.

Manually checking for defects on industrial parts such as steel surfaces is ineffective, error-prone, and can damage a company’s reputation. Current automated methods often lack accuracy or real-time detection capabilities. Early detection allows for timely corrective action, such as removing defective parts or adjusting production parameters which can streamline the manufacturing process and improve brand reputation, customer satisfaction, and compliance with industry standards. This paper presents MoL-YOLOv7 (MobileNet integrated with attention and loss function to You Only Look Once version 7), a deep learning model for accurate and real-time detection of steel defects. MoL-YOLOv7 modifies the YOLOv7 model by inserting a MobileNet block that reduces computational complexity while maintaining accuracy, allowing for faster detection. Adding the SimAm (Simple Parameter free Attention module) attention mechanism to the MobileNet block refines feature representations for complex tasks such as steel defect detection. Finally, replacing loss functions with EIoU (Effective IoU), WIoU (Wise-IoU), and SIoU (Scylla-IoU) improves the localization accuracy and addresses the class imbalance in the data. The modified model achieves high accuracy and real-time detection, enabling a streamlined defect detection process. Experimental results show that the modified model involving different loss functions used in this work achieves high accuracy, i.e. 0.5% to 3.5% higher than the original model YOLOv7. The superiority and validity of our modified model are demonstrated by comparison with other attention mechanisms and loss functions integrated into YOLOv7, and also on different texture datasets, putting forward a modified method to detect surface defects on steel strips in daily operations.

Steel is widely used in the aerospace, machinery and automotive industries. Surface defects not only have a negative impact on the appearance of steel but also significantly reduce its wear resistance, high temperature resistance, corrosion resistance and fatigue strength. Therefore, the detection of steel surface defects is very important to improve the quality of steel production. The limited availability of surface defect samples in the industrial sector poses significant challenges for the accurate detection of defects in high-quality materials. In addition, the existing defect detection model is highly complex and not easy to deploy. To solve this problem, a lightweight defect detection network suitable for steel defects is proposed. The cross-layer feature fusion (CFF) in the design enables effective utilisation of multi-layer semantic features, facilitating the detection of small defects in steel. Secondly, a new loss function is designed to make up for the problems of small data volume and uneven data distribution. The experimental results demonstrate that the steel surface defect detection method proposed in this paper achieves the highest detection performance on widely used public datasets such as RSDDS, NEUS and NRSD-CR(test), while maintaining the lowest model complexity. [ABSTRACT FROM AUTHOR]

To improve the accuracy of steel surface defect detection, an improved model of multi-directional optimization based on the YOLOv8 algorithm was proposed in this study. First, we innovate the CSP Bottleneck with the two convolutions (C2F) module in YOLOv8 by introducing deformable convolution (DCN) technology to enhance the learning and expression ability of complex texture and irregular shape defect features. Secondly, the advanced Bidirectional Feature Pyramid Network (BiFPN) structure is adopted to realize the weight distribution learning of input features of different scales in the feature fusion stage, allowing for more effective integration of multi-level feature information. Next, the BiFormer attention mechanism is embedded in the backbone network, allowing the model to adaptively allocate attention based on target features, such as flexibly and efficiently skipping non-critical areas, and focusing on identifying potentially defective parts. Finally, we adjusted the loss function from Complete-Intersection over Union (CIoU) to Wise-IoUv3 (WIoUv3) and used its dynamic non-monotony focusing property to effectively solve the problem of overfitting the low quality target bounding box. The experimental results show that the mean Average Precision (mAP) of the improved model in the task of steel surface defect detection reaches 84.8%, which depicts a significant improvement of 6.9% compared with the original YOLO8 model. The improved model can quickly and accurately locate and classify all kinds of steel surface defects in practical applications and meet the needs of steel defect detection in industrial production. [ABSTRACT FROM AUTHOR]

Industrial defect detection is a hot topic in the field of computer vision. It is a challenging task due to complex features and many categories of industrial defects. In this paper, a deep learning model based on the multiscale feature extraction module is introduced for steel surface defect detection. The main focus on the feature extraction capability of the model and feature fusion capability to improve the accuracy of the model for steel surface defect detection. First, to improve the feature extraction ability of the model, a multiscale feature extraction (MSFE) module is introduced. The MSFE module can effectively extract multiscale features through three branches that have different convolution kernel sizes. Second, an efficient feature fusion (EFF) module is proposed to optimize feature fusion by adding features from the backbone network to the neck network. Third, this paper puts forward a new Bottleneck module by reducing the normalization layer and activation function in the original Bottleneck module. Finally, the backbone network is deepened to further enhance the feature extraction ability of the model. Extensive experiments are conducted on the public NEU-DET dataset. The experimental results validate the effectiveness of the designed modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves optimal accuracy(73.08% mAP@0.5) while maintaining a small number of parameters. [ABSTRACT FROM AUTHOR]

Abstract Industrial defect detection is a hot topic in the field of computer vision. It is a challenging task due to complex features and many categories of industrial defects. In this paper, a deep learning model based on the multiscale feature extraction module is introduced for steel surface defect detection. The main focus on the feature extraction capability of the model and feature fusion capability to improve the accuracy of the model for steel surface defect detection. First, to improve the feature extraction ability of the model, a multiscale feature extraction (MSFE) module is introduced. The MSFE module can effectively extract multiscale features through three branches that have different convolution kernel sizes. Second, an efficient feature fusion (EFF) module is proposed to optimize feature fusion by adding features from the backbone network to the neck network. Third, this paper puts forward a new Bottleneck module by reducing the normalization layer and activation function in the original Bottleneck module. Finally, the backbone network is deepened to further enhance the feature extraction ability of the model. Extensive experiments are conducted on the public NEU-DET dataset. The experimental results validate the effectiveness of the designed modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves optimal accuracy(73.08% mAP@0.5) while maintaining a small number of parameters.

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

Aiming at the problems of low detection efficiency and poor detection accuracy caused by texture feature interference and dramatic changes in the scale of defect on steel surfaces, an improved YOLOv5s model is proposed. In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path spatial pyramid pooling module to adapt to the scale variation of steel surface defects. Finally, we propose a training strategy that applies different kernel sizes for feature maps of different scales so that the receptive field of the model can adapt to the scale changes of the feature maps to the greatest extent. The experiment on the NEU-DET dataset shows that our model improved the detection accuracy of crazing and rolled in-scale, which contain a large number of weak texture features and are densely distributed by 14.4% and 11.1%, respectively. Additionally, the detection accuracy of inclusion and scratched defects with prominent scale changes and significant shape features was improved by 10.5% and 6.6%, respectively. Meanwhile, the mean average precision value reaches 76.8%, compared with the YOLOv5s and YOLOv8s, which increased by 8.6% and 3.7%, respectively. [ABSTRACT FROM AUTHOR]

Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement. [ABSTRACT FROM AUTHOR]

In today's large-scale popularity of machinery manufacturing, people's demand for the detection of steel plate surface defects has gradually increased over time. Nowadays, the production of steel plates in various fields has to be in line with the actual product application after multiple rounds of testing. However, due to material, machine, and processing technology problems, defects usually appear on the surface of the steel plate during the process of mass production of steel plates. These defects not only affect the beauty and the esthetics, but also they have an unfavorable impact on the performance of the steel plate, and can even lead to a series of safety problems. Thus, we need to improve the requirements for the detection of defects on the surface of the steel plate. With the improvement of computer computing power and automation technology, deep learning has taken advantage of this development. It is one of the most representative fields of artificial intelligence, excelling in image classification, target detection, segmentation, and tracking of targets. For defect detection tasks with a small number of labeled samples, a large number of unlabeled samples, and a wide range of data sources, the unsupervised domain adaptive method was adopted to realize knowledge transfer between data in different domains to solve the above problems. The unsupervised domain adaptive method can train the defect segmentation model effectively by using labeled source domain data and unlabeled target domain data. In this way, the model can obtain better segmentation results in the target domain without increasing other costs. From the point of view of scene setting, most of the unsupervised domain adaptive methods are focused on a single-source single-target domain. To adapt to the real factory environment, we extended the domain adaptive method of steel plate surface defect feature learning to multi-source single-target and multi-source multi-target domains. Hence, we realized the effective migration of defect features among data in different domains and provided corresponding solutions for segmentation detection of multi-source data sets with few samples and no labels. Multi-target domain technology stands out in the field of related technologies with its powerful memory capability, nonlinear mapping capability, self-learning capability, and robustness. For training, we fed a small group of sample data into the network of generative adversarial networks. In this training, the low-level network could automatically learn the detailed features of the data, and the high-level network could automatically learn the abstract features. To improve industrial automation for the effective detection of steel plate surface defects, a deep learning-based algorithm was proposed for the detection of steel plate surface defects. The method was based on multi-target domain instance segmentation, which could well retain the texture and background integrated features in the visual features of steel plates; the dynamic random operation of the final pooling layer eliminated the multi-scale problem of steel plate vision; the multi-dimensional feature fusion of hierarchy ensured the diversity of features; thus, the recognition accuracy of steel plate surface defects was improved. In the image dataset of the detection of steel plate surface defects, the proposed method achieved outstanding performance compared with the other methods, which proved the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The production and manufacturing processes of steel inevitably generate various types of surface defects. The real-time and accurate detection of these surface defects is of great practical significance. To realise real-time detection of steel surface defects with significant differences in shape and size on resource constrained edge computing equipment, this paper proposes a lightweight real-time steel surface defect detection model SD-YOLO based on a dynamic parameterisation strategy. Firstly, a Dynamic Parameterised Enhancement Module is proposed, which dynamically assigns routing weights to parallel convolutional kernels based on input features, thereby enhancing the representation of defect features in the feature map and improving the network's ability to capture rich and detailed features. Secondly, the Efficient Intersection over Union loss function is employed to optimise the regression process of the prediction boxes. This enhances the model's fitting performance on bounding boxes with significant aspect ratio differences and improves the accuracy of detecting defects of various scales. Experimental results indicate that for the NEU-DET and GC10-DET datasets, SD-YOLO achieves a mean average precision of 83.1% and 74.1% respectively, with a stronger focus on defective regions, and detection speeds of 169.5 Frames Per Second (FPS) and 178.6 FPS, respectively. When SD-YOLO is deployed on the NVIDIA Jetson Orin NANO, the detection speed reaches 33.9 FPS and 66.7 FPS respectively, and maintains the same detection accuracy as the server-side, which realises real-time, accurate, and automatic detection of steel surface defects on edge computing devices with limited computational resources. Furthermore, SD-YOLO also demonstrates excellent generalisation ability and accuracy on images of steel surface defects collected in real industrial environments. In conclusion, SD-YOLO provides a practical and effective solution for real-time steel surface defect detection in resource-constrained environments, making it highly suitable for deployment in industrial applications. Source code is available at https://github.com/Xcy0512/SD-YOLO. [ABSTRACT FROM AUTHOR]

Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawbacks, e.g., the dataset is limited accessible or small-scale public, and related works focus on developing models but do not deeply take into account real-time applications. In this paper, we investigate the feasibility of applying stage-of-the-art deep learning methods based on YOLO models as real-time steel surface defect detectors. Particularly, we compare the performance of YOLOv5, YOLOX, and YOLOv7 while training them with a small-scale open-source NEU-DET dataset on GPU RTX 2080. From the experiment results, YOLOX-s achieves the best accuracy of 89.6% mAP on the NEU-DET dataset. Then, we deploy the weights of trained YOLO models on Nvidia devices to evaluate their real-time performance. Our experiments devices consist of Nvidia Jetson Nano and Jetson Xavier AGX. We also apply some real-time optimization techniques (i.e., exporting to TensorRT, lowering the precision to FP16 or INT8 and reducing the input image size to 320 × 320) to reduce detection speed (fps), thus also reducing the mAP accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection efficiency and poor detection accuracy caused by texture feature interference and dramatic changes in the scale of defect on steel surfaces, an improved YOLOv5s model is proposed. In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path spatial pyramid pooling module to adapt to the scale variation of steel surface defects. Finally, we propose a training strategy that applies different kernel sizes for feature maps of different scales so that the receptive field of the model can adapt to the scale changes of the feature maps to the greatest extent. The experiment on the NEU-DET dataset shows that our model improved the detection accuracy of crazing and rolled in-scale, which contain a large number of weak texture features and are densely distributed by 14.4% and 11.1%, respectively. Additionally, the detection accuracy of inclusion and scratched defects with prominent scale changes and significant shape features was improved by 10.5% and 6.6%, respectively. Meanwhile, the mean average precision value reaches 76.8%, compared with the YOLOv5s and YOLOv8s, which increased by 8.6% and 3.7%, respectively.

Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawbacks, e.g., the dataset is limited accessible or small-scale public, and related works focus on developing models but do not deeply take into account real-time applications. In this paper, we investigate the feasibility of applying stage-of-the-art deep learning methods based on YOLO models as real-time steel surface defect detectors. Particularly, we compare the performance of YOLOv5, YOLOX, and YOLOv7 while training them with a small-scale open-source NEU-DET dataset on GPU RTX 2080. From the experiment results, YOLOX-s achieves the best accuracy of 89.6% mAP on the NEU-DET dataset. Then, we deploy the weights of trained YOLO models on Nvidia devices to evaluate their real-time performance. Our experiments devices consist of Nvidia Jetson Nano and Jetson Xavier AGX. We also apply some real-time optimization techniques (i.e., exporting to TensorRT, lowering the precision to FP16 or INT8 and reducing the input image size to 320 × 320) to reduce detection speed (fps), thus also reducing the mAP accuracy.

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, we propose a framework to extract the features in an unsupervised (or self-supervised) manner using deep learning, particularly stacked LSTM Autoencoder Networks. The compressed representation of the time-series data obtained from LSTM Autoencoders are then provided to Deep Feedforward Neural Networks for classification. We apply the proposed framework on sensor time series data from the process industry to detect the quality of the semi-finished products and accordingly predict the next production process step. To validate the efficiency of the proposed approach, we used real-world data from the steel industry. [ABSTRACT FROM AUTHOR]

• A new steel surface defect detection framework, exploiting the idea of domain adaptation learning, is proposed. • A domain classifier is added to realize the cross-domain recognition. • A constraint on label probability distribution is applied to realize the cross-task recognition. • The normal distribution and the quadratic function are used to optimize the loss. • The adaptive learning rates are proposed based the loss and the weight. Automatic inspection methods based on machine vision have been widely employed for steel surface defect detection. The central purpose of these methods is to extract features to represent different defects. However, current methods depend on machine learning that demands handcrafted features and overlooks the domain shift. In this paper, we propose a new method combining domain adaptation (DA) and adaptive convolutional neural network (ACNN), called DA-ACNN, to achieve steel surface defect detection. The convolutional neural network (CNN) is used as the backbone. To account for the lack of labels in a new domain, we introduce an additional domain classifier and a constraint on label probability distribution to achieve the cross-domain and cross-task recognition. The normal distribution and the quadratic function are used to optimize the loss to improve the network performance. Adaptive learning rates based on the loss and the weight, respectively, are proposed to minimize the losses of DA and classification. We conducted experiments on steel surface defect datasets to validate the effectiveness of DA-ACNN. Compared with the classical CNN and other approaches, the results demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models. [ABSTRACT FROM AUTHOR]